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Computation of expected shortfall by fast detection of worst scenarios

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  • Bruno Bouchard
  • Adil Reghai
  • Benjamin Virrion

Abstract

We consider multi-step algorithms for the computation of the historical expected shortfall. At each step of the algorithms, we use Monte Carlo simulations to reduce the number of historical scenarios that potentially belong to the set of worst-case scenarios. We show how this can be optimized by either solving a simple deterministic dynamic programming algorithm or in an adaptive way by using a stochastic dynamic programming procedure under a given prior. We prove ${{\mathbb L}}^{p} $Lp-error bounds and numerical tests are performed.

Suggested Citation

  • Bruno Bouchard & Adil Reghai & Benjamin Virrion, 2021. "Computation of expected shortfall by fast detection of worst scenarios," Quantitative Finance, Taylor & Francis Journals, vol. 21(7), pages 1087-1108, July.
    • Handle: RePEc:taf:quantf:v:21:y:2021:i:7:p:1087-1108
    DOI: 10.1080/14697688.2021.1880618
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